Redirect | MIT Technology Overview

I became increasingly convinced that going to MIT was a necessary part of making my dream come true. And yet it was not destined to happen.

This is one of the tricks of the far horizon problem: the first plan you come up with often doesn’t work.

Once I got over my initial shock and disappointment, I realized that my long-term goal wasn’t entirely doomed; I was accepted into several other wonderful schools. Determined to make the most of the situation, I tried to be systematic in my decision. I created a detailed set of criteria to capture everything I thought I wanted in college and gave each of them a weight based on importance. I then browsed online forums, talked to current students, and even visited all of my options in person. I entered numbers for each of my criteria and calculated the final score for each school. Not surprisingly, these scores told me that I should choose a major technical institution like MIT. However, I realized that how this answer because I was falling more and more in love with one small liberal arts school. I tried fiddling with the numbers on my spreadsheet, but no matter what, her score never made it to the top of the list; my criteria were simply stacked against him. Finally, on the eve of the decision deadline, I deleted my detailed spreadsheet and followed my heart.

Another feature of long-term problems: everything changes when you start solving them. No matter how sure you are in advance of your opinion, any of them can change in the face of new information.

As it turns out, going to this liberal arts school turned out to be one of the best decisions I’ve ever made. I had to study deeply technical subjects, but I was also interested in philosophy, contemplative practice, and even public speaking. I’ve had to work and learn from people who are incredibly passionate about technology and inventions, but I’ve also made friends with people who have expanded my horizons into areas I didn’t even know existed. And it so happened that I fell in love and experienced grief. All in all, I had a transformative experience that made me not only a better engineer and scientist, but also – I like to think – a better, more thoughtful, and more conscious person.

My student years also helped crystallize the path to becoming an inventor. I became interested in research in robotics and artificial intelligence and realized that I was very passionate about fulfilling the dream of creating personal home robots. I met inspiring mentors who showed me that becoming a researcher in this field is a viable career option. I have had to work on interesting problems in both academic and industry settings, which has made me realize how much I still have to learn about my interests. In an interesting turn of events, I decided to apply for a PhD at MIT and was accepted into CSAIL, where I now work on complex but important problems with passionate people on a daily basis.

Looking back, I am always amazed at the number of options for the development of events. I could have been accepted into MIT in high school. I could have chosen one of the other schools that accepted me. I could choose a different major or join a different study group in college. I had no idea how any of these decisions would turn out, and yet each of them was critical in getting me to where I am today. Perhaps I would have reached the same point in many other scenarios. Perhaps some of these versions of me would be more satisfied with their travels than I am with mine. I guess I’ll never know. However, I know what really Indeed how it version of me, and I’m grateful for the random, unpredictable, and sometimes painful events that brought me here.

I suggest that this is another feature of long-term problems: there are an infinite number of ways to solve them, but it is never completely clear what the next step to take on any of these paths. We just need to decide in the moment as much as possible and trust that all the dots will come together in hindsight.

Now, if only I could make my robots understand all of this.

Nishant Kumar is a sophomore in the Learning and Intelligent Systems group at CSAIL, where he is working on making robots smarter. In addition to research, he enjoys reading and writing science fiction, playing table tennis, and cooking spicy food.

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